When each concept fragment is processed, edges are added between the current concept fragment and its predecessors.

how to treat its predecessors is a difficult problem.


It is linear in the length of sequence of concept fragments.

the constant in the O is relatively large.


(2016) also presents an incremental AMR parser based on a simple transition system for dependency parsing.

compared to our parser, their parser cannot parse non-projective graphs, resulting in a limited coverage.


This effect holds when we run the same model on data subsets consisting only of dogmatic or non-dogmatic users, and also when we conservatively remove all words used by B from A's response (i.e., controlling for quoting effects).

to the computational models we have presented, dogmatism is usually measured in psychology through survey scales, in which study participants answer questions designed to reveal underlying personality attributes (Rokeach, 1954).


This information is pivotal when tailoring search results to the preferences of specific individuals.

in some cases a user may have very limited previous interactions with the system.


A similar approach was also explored in (Noll and Meinel, 2007) where the system performed re-ranking of Google search results based on social bookmarks and tags harvested from del.icio.us 1 .

the data sparsity problem poses a challenge to this approach as not all Web pages returned by search engines are tagged in the del.icio.us dataset.


Researchers have considered tag-tag relationships for personalized query expansion, by selecting the most related tags from a user's profile (Bender et al., 2008;Bertier et al., 2009).

tags cannot be relied upon to consistently provide precise descriptions of resources for use when searching.


However, all of the previously mentioned systems consider constructing user profiles solely from past usage information.

in this paper we extend personalized web search using social data by exploiting an external knowledge base to enhance the user profile.


are mixed together to infer unified latent topics.

the MEUP model may miss important information when the topics are learned.


The results also show that the improvements over baseline models in the User-SMALL group are more noticeable than in the User-LARGE group.


Hence, sophisticated approximation techniques based on algorithms such as belief propagation and dual decomposition have been employed.

we propose a simple greedy search approximation for this problem which is very intuitive and easy to implement.


We first present a basic greedy algorithm that relaxes the global graph-based objective (Section 3).

as this simple algorithm does not provide a realistic estimation of the impact of an arc selection on uncompleted high-order structures in the partial parse forest, it is not competitive with state of the art approximations.


Both "国 航" and "中国国航" are appropriate abbreviations for "中 国 国 际 航 空 公 司(Air China)".

"国 航 司" is not a Chinese word and cannot be understood by humans.


Johansson (2013) introduced path-based feature templates in using one parser to guide another.

to the above discrete methods, our neural stacking method offers further feature integration by directly connecting the feature layer of the source tagger with the input layer of the target tagger.


The improvement over the best formmodel reaches up to 6 points (e.g., Czech nouns).

to form sum, LAMB improves over form opt on German nouns.


Thanks to the CTB-train sentences, the model may be able to choose the correct tag, but inevitably becomes more indecisive at the same time due to the PD-train sentences.

the offline pruning approach directly uses two baseline models for pruning, which is a job perfectly suitable for the baseline models.


This work is also closely related with multi-task learning, which aims to jointly learn multiple related tasks with the benefit of using interactive features under a share representation (Ben-David and Schuller, 2003;Ando and Zhang, 2005;Parameswaran and Weinberger, 2010).

as far as we know, multi-task learning usually assumes the existence of data with labels for multiple tasks at the same time, which is unavailable in our scenario, making our problem more particularly difficult.


Hence, these summaries are as coherent as the author intends them to be, but they are not informative.

cP 3 is very close in coherence to Lead indicating that our strategy is successful.


(2015b) is the only work exactly dealing with the news stream summarization challenge.

they studied the problem on a static timestamped corpus instead of on a dynamic text stream and their proposed pipeline-style approach cannot be applied on a real-time text stream due to high complexity in time and space.


In particular, in previous work (Zhang and Wallace, 2015) we observed that CNN outperforms SVM uniformly on sentence classification tasks (the average sentence-length in these datasets was about 10).

in the datasets we consider in the present paper, documents often comprise hundreds of sentences, each in turn containing multiple words.


4 with respect to µ i and set it to zero, we obtain, which implies that the MLE of µ i is the sample mean.

since we are dealing with a set of weighted samples, the sample mean is replaced by the weighted average (the weights w l are assumed to add up to one): Our model yields an intuitive result: to estimate the mean vector µ i of u i , we can simply take the weighted average of the latent factors u l from the nearest-neighbors as an estimator, where the weights are the similarity scores between the textual proles of user i and its neighbors.


For all baseline methods, we use K to denote the dimensionality of the latent variables.

when discussing about NT-MF, since the number of topics can be different from the number of user latent factors, we use T to denote the former and K to denote the latter to avoid confusion.


In Figure 6, most negative cues have good PCSs (higher than 70%).


data input (Vinyals et al., 2015;Zhou and Xu, 2015;Rocktäschel et al., 2015).

due to the complexity of the neural networks and the lack of effective analytic methodology, little is known about how a sequential model of sentence, such as recurrent neural network, captures linguistic knowledge.


Both some of the internal neurons of the memory cell in the language model and the sentiment model emerge to be sensitive to the length of the input sequence, as we expected, since the sentence length is a non-linguistic features shared by all the sequential input.

different optimization objectives force the models to represent and capture the linguistic properties of different aspects.


Their visualization results show some interesting properties of the memory neurons in LSTM unit.

their exploration on character-based model does not intend to correlate high-level linguistic knowledge, which are intuitively required for sequential modelling of a sentence.


In addition to the previously mentioned methods, a few researchers have studied the problem of extracting keyphrases from collections of tweets (Zhao et al., 2011;Bellaachia and Al-Dhelaan, 2012).

to traditional web applications, Twitter-like services usually limit the content length to 140 characters.


Because multiple tweets are usually organized by topic, many document-level approaches can also be adopted to achieve the task.

with the previous methods, Marujo et al.


They used several unsupervised methods and word embeddings to construct features.

the proposed method worked on the word level.


One way to capture the contextual information of a word sequence is to concatenate neighboring features as input features for a deep neural network.

the number of parameters rapidly increases according to the input dimension.


CRF inference with NN models cited previously do improve exact phrase labeling.

better ways of modeling the pairwise potential functions of CRFs might lead to improvements in labeling rare entities and detecting exact phrase boundaries.


Although the work (Wang et al., 2011;Rayana and Akoglu, 2015) have tried to employ graph structure to consider the interac-tions among the reviewers and products, it is a kind of local interaction defined within the same product review page.

the interaction among the reviewers and products from different review pages also provides much useful and global information, which is ignored by the previous work.


They find that the average review length of the spammers is quite short compared with non-spammers.

once a crafty spammer realizes that he left this type of footprint, he could produce a review that is as long as the non-  spammers to pretend to be a normal reviewer.


For example, the tweet "@realDon-aldTrump is the only honest voice of the @GOP" expresses a positive stance towards the target Donald Trump.

when stance is annotated with respect to Hillary Clinton as the implicit target, this tweet expresses a negative stance, since supporting candidates from one party implies negative stance towards candidates from other parties.


A common stance detection approach is to treat it as a sentence-level classification task similar to sentiment analysis (Pang and Lee, 2008;Socher et al., 2013).

such an approach cannot capture the stance of a tweet with respect to a particular target, unless training data is available for each of the test targets.


The correct event type for the trigger word "leave" in this case is "End-Org".

the previous CNN models might not be able to detect "leave" as an event trigger or incorrectly predict its type as "Movement".


For these types, TransInit successfully recognises PERSON as the most related type to DOCTOR, as well as CARDINAL as the most related type to ID.

ccA often fails to identify meaningful type alignments, especially for small training data sizes.


For example, "burning tires" is a concrete phrase that would not fall into the category of more abstract events.

"gathered at site" is certainly more ambiguous.


Much recent research in event timeline generation  suggests the usefulness of subevents in improving quality and completeness of automatically generated event summaries.

they often focus on a different notion of subevents that broadly covers pre-condition events and consequence events and is temporally-based.


In conventional ZSL, a novel word can be matched against images by projecting it into image space, and sorting images by their distance to the word (vector), or likelihood under the word (Gaussian).

results may be unreliable when used with polysemous words, or words with large appearance variability.


With regards to efficiency, our model is fast to train if fixing pre-trained word-Gaussians and optimising only the cross-modal mapping A.

training the mapping jointly with the word-Gaussians comes at the cost of updating the representations of all words in the dictionary, and is thus much slower.


Pairwise Models vs Unary Models As shown in Table 1, the pairwise models based on Skip-Thought features outperform the unary models in our task.

the Pairwise Order Model performs worse than the unary Skip-Thought model, suggesting that the Skip-Thought features, which encode context of a sentence, also provide a crucial signal for temporal ordering of story sentences.


The fact that some (it's unclear how many) of these spatial distributions end up being interpretable is simply fortuitous.

we study where humans choose to look to answer visual questions.


In LSTM, gating mechanism is used to control the information flow such that gradient vanishing problem in vanilla RNN is better handled, and long range dependency is better captured.

as empirically verified by previous works and our own experiments, to obtain fairly good results, training LSTM RNN needs carefully designed optimization procedure (Hochreiter et al., 2001;Pascanu et al., 2013;Dai and Le, 2015;Laurent et al., 2015;He et al., 2016;Arjovsky et al., 2015), especially when faced with unfolded very deep architectures for fairly long sequences (Dai and Le, 2015).


Therefore, a forgetting mechanism to "forget" less critical historical information, as is employed in LSTM (controlled by the forget gate f t ), becomes necessary.

while LSTM benefits from the flexible gating mechanism, its parametric nature brings optimization difficulties to cope with fairly long sequences, whose long range information dependencies could be better captured by identity connections.


Note that in Figure 1, a straightforward approach is to replace F with an LSTM unit.

our preliminary experiments do not achieve satisfactory results.


From this table, we have the following observations and analysis:    half of the model parameters, indicating that residual network structure, with connecting mechanism to enhance the information flow, is also an effective approach for sequence learning.

the fact that it fails to significantly outperform other models (as it does in image classification) implies that forgetting mechanism is desired in recurrent structures to handle multiple inputs.


For tasks with shorter sequences such as AG's News, the improvement is limited.

the improvements get more significant with the growth of sequence lengths among different datasets 4 , and the performance is particularly good in P-MNIST with very long sequences.


On the medical test set which has 9 times higher OOV ratio, the perplexity reduction shows a similar trend.

these reductions vanish when an in-domain test set is used.


Empirically, for non-convex objectives, different approaches may arrive at different solutions.

for convex loss functions, our objective (Equation 2) is also convex, and all approaches should share the same solution.


Canonical segmentation has several representational advantages over surface segmentation, e.g., whether two words share a morpheme is no longer obfuscated by orthography.

it also introduces a hard algorithmic challenge: in addition to segmenting a word, we must reverse orthographic changes, e.g., mapping achievability →achieveableity.


Inspired by the recent success of neural encoder-decoder models (Sutskever et al., 2014) for sequence-to-sequence problems in NLP, we design a neural architecture for the task.

a naïve application of the encoder-decoder model ignores much of the linguistic structure of canonical segmentation-it cannot directly model the individual canonical segments, e.g., it cannot easily produce segment-level embeddings.


(2011) who show that POS, chunking and semantic role information can bring benefit to each other in joint neural training.

to their results (SRL 74.15→74.29, POS 97.12→97.22, CHUNK 93.37→93.75), we find that parsing and SRL benefit relatively more from each other (SRL 72.72→73.84,DEP 84.33→85.15).


These pairs usually get low human-labeled similarity scores.

splitting the words in such pairs into characters, and further the characters into radicals will not help to effectively identify the dissimilarity between them.


1 ' The speaker makes a subtle, contemptuous remark about the sense of humor of the listener.

absence of sentiment words makes the sarcasm in this sentence difficult to capture as features for a classifier.


(2013) who give a bootstrapping algorithm that discovers a set of positive verbs and negative/undesirable situations.

this simplification (of representing sarcasm merely as positive verbs followed by negative situation) may not capture difficult forms of context incongruity.


by a simple Naive Bayes classifier trained on word and character n-gram data (Lui and Baldwin, 2012): a document of significant length will be quickly disambiguated based on its vocabulary (King et al., 2014).

social media platforms like Twitter produce data sets in which individual documents are extremely short, and language use is idiosyncratic: LID performance on such data is dramatically lower than on traditional corpora (Bergsma et al., 2012;Carter et al., 2013).


Intuitively, this structural, syntactic, and semantic information underlying input text has the potential for improving the quality of NLG tasks.

to the best of our knowledge, there is no clear evidence that syntactic and semantic information can enhance the recently developed encoder-decoder models in NLG tasks.


Embeddings have also been explored to extract hypernym relations from general corpora (Rei and Briscoe, 2014).

word embeddings have not been used for generating lexical simplifications.


(2016) show that by combining SSWE, LSTM outperforms traditional SVM model.

using LSTM alone does not give significantly more accuracies compared to SVM.


The gap shrinks as the amount of supervised data is increased, which is as expected.

using a large amount of extra, generated data from an approximating distribution (SEQ4+) does not help as much initially when compared with the unsupervised data from the true distribution.


Our work is also conceptually related to work on semantic parsing -mapping natural language text to a formal meaning representation (Wong and Mooney, 2007;Clarke et al., 2010;Cai and Yates, 2013;Kwiatkowski et al., 2013;Goldwasser and Roth, 2011).

as mentioned earlier, there are some significant differences in the task definition that necessitate the development of a new approach.


We use approximate inference, first enumerating possible trigger lists, and then equation trees, and find the best scoring structure.

this method did not outperform the pipeline method.


Normalizing consists of a battery of deterministic steps implemented using syntactic dependencies and semantic roles.

with previous work (Section 2), our normalization is fully automated.


The direct word-demographics analysis gives useful validation that the demographic information may yield dialectal corpora, and the seedlist approach can assemble a set of users with heavy dialectal usage.

the approach requires a number of ad-hoc thresholds, cannot capture authors who only occasionally use demographically-aligned language, and cannot differentiate language use at the message-level.


We define: • λ(T ), σ(T ) and (T ) as the subsets of T that respectively contain all tweets in language λ, script σ and sentiment .

• The preference towards a language-script pair λσ for expressing a type of sentiment is given by the probability pr(λσ), which defines the prior probability of choosing λσ for a tweet is dependent on a large 2 Tweets in mixed script are rare and hence we do not include a symbol for it, though the framework does not preclude such possibilities.


Our results indicate a strong preference for using Hindi, L1 for the users from whom these tweets come, for expressing negative sentiment, including swearing.

we do not observe any particular preference towards Hindi for expressing opinions.


This is perhaps the work that is closest to ours in the existing literature.

their model differs from ours in that it uses a max-pooling layer that picks the most activated feature across time, thus ignoring temporal information, whereas we explicitly avoid doing so.


For the kernel size, we set it to w = 3 words for the simple CNN (out of options 3, 5, 7, 9), whereas for the COM variant we use w = 3 and 5, based on experimentation on PTB.

we observed the models to be generally robust to this parameter.


A final (smaller) improvement comes from combining kernels of size 3 and 5, which can be attributed to a more expressive model that can learn patterns of n-grams of different sizes.

to the successful two variants above, the multi-layer CNN did not help in better capturing the regularities of text, but rather the opposite: the more convolutional layers were stacked, the worse the performance.


see k = 256 of k = 512) on the first two datasets.

the LSTM can be easily scaled using larger models, as shown in Zaremba et al.


Our results show a solid 11-26% reduction in perplexity with respect to the feed-forward model across three corpora of different sizes and genres when the model uses MLP Convolution and combines kernels of different window sizes.

even without these additions we show CNNs to effectively learn language patterns that allow it to significantly decrease the model perplexity.


As these models are directly predicting p with no concept of mixture weights λ, they cannot be interpreted as MODLMs as-is.


Although single-pass reading plays a crucial role when we just want the general meaning and do not necessarily need to understand every single point of the text, it is not enough for tackling tasks that need a deep analysis of the text.

with single-pass reading, repeated reading involves the process where learners repeatedly read the text in detail with specific learning aims, and has the potential to improve readers' reading fluency and comprehension of the text (National Institute of Child Health and Human Development, 2000;LaBerge and Samuels, 1974).


Arg-1 : the use of 900 toll numbers has been expanding rapidly in recent years Arg-2 : for a while, high-cost pornography lines and services that tempt children to dial (and redial) movie or music information earned the service a somewhat sleazy image (Comparison -wsj 2100) To identify the "Comparison" relation between the two arguments Arg-1 and Arg-2, the most crucial clues mainly lie in some content, like "expanding rapidly" in Arg-1 and "earned the service a somewhat sleazy image" in Arg-2, since there exists a contrast between the semantic meanings of these two text spans.

it is difficult to obtain sufficient information for pinpointing these words through scanning the argument pair left to right in one pass.


We perform significance test for these two improvements, and they are both significant under one-tailed t-test (p < 0.05).

when adding the third attention level, the performance does not promote much and almost reaches its plateau.


Sluicing antecedent selection might appear simple -after all, it typically involves a sentential expression in the nearby context.

analysis of the annotated corpus data reveals surprising ambiguity in the identification of the antecedent for sluicing.


Looking more generally, there is an obvious potential connection between antecedent selection for ellipsis and the problem of coreference resolution (see Hardt (1999) for an explicit theoretical link between the two).

entity coreference resolution is a problem with two major differences from ellipsis antecedent detection: a) the antecedent and anaphor often share a variety of syntactic, semantic, and morphological characteristics that can be featurally exploited; b) entity expressions in a text are often densely coreferent, which can help provide proxies for discourse salience of an entity.


The solution to antecedent selection that we have presented here provides a starting point for addressing the problem of resolution, in which the content of the sluice is filled in.

even if the correct antecedent is selected, the missing content is not always an exact copy of the antecedent -often substantial modifications will be required -and an effective resolution system will have to negotiate such mismatches.


Although the existing works also exploited such word sequences, they used only particular types of information from them as features based on the researchers' linguistic insights.

we minimized such feature engineering due to using an MCNN.


In addition, existing works have exploited the dependency path between a predicate and a candidate antecedent either by encoding such paths to the set of binary features of the words that appear in the path (Iida and Poesio, 2011) or by mining from the paths the sub-trees that effectively discriminate zero anaphoric relations (Iida et al., 2006).

both methods just focus on the dependency paths between a predicate and a candidate antecedent without exploiting other structural fragments in the dependency tree representing a target sentence, whereas our method uses the text fragments that cover the entire dependency tree.


10 The PR-curves of our method and the single-column baseline were plotted just by altering the threshold parameters in Step 4 of our method (See Section 3).

the PR-curve of Ouchi's method cannot be easily plotted because it gives a score to each sentence, not to each zero anaphoric relation.


Longer utterances seem to carry enough information for our DTW-based measure to function properly.


Some manual measures ask annotators to explicitly mark errors, but this has been found to have even lower agreement than ranking (Lommel et al., 2014).

while providing the gold standard for MT evaluation, human evaluation is not a scalable solution.


The Abstract Meaning Representation (AMR) (Banarescu et al., 2013) shares UCCA's motivation for defining a more complete semantic annotation.

using AMR is not optimal for defining a decomposition of a sentence into semantic units as it does not anchor its semantic symbols in the text, and thus does not provide clear decomposition of the sentence into sub-spans.


Table 3 also shows that the overall IAA is similar for all languages, presenting good agreement (0.6-0.7).

there are differences observed when we break down by node type.


Normally monolingual word embeddings are trained on billions of words.

obtaining that much monolingual data for a low-resource language is infeasible.


We observed similar trend using Panlex and Wiktionary dictionary in our model.

using Panlex results in much better performance.


10 Low-resource languages Our model exploits dictionaries, which are more widely available than parallel corpora.

the question remains as to how well this performs of a real low-resource language, rather than a simulated condition like above, whereupon the quality of the dictionary is likely to be worse.


In practice, a simple beam search procedure that explores K prospective histories at each time-step has proven to be an effective decoding approach.

as noted above, decoding in this manner after conditional languagemodel style training potentially suffers from the is-sues of exposure bias and label bias, which motivates the work of this paper.


Ideally we would train by comparing the gold sequence to the highest-scoring complete sequence.

because finding the argmax sequence according to this model is intractable, we propose to adopt a LaSO-like (Daumé III and Marcu, 2005) scheme to train, which we will refer to as beam search optimization (BSO).


For both the baseline and BSO models we enforce this constraint at testtime.

we also experiment with constraining the BSO model during training, as described in Section 4.2, by defining the succ function to only allow successor sequences containing un-used words in the source sentence.


During training the model uses dynamic programming to marginalize over permutations of the null symbols, while beam search is employed during decoding.

our model defines a separate latent alignment variable, which adds flexibility to the way the alignment distribution can be defined (as a geometric distribution or parameterised by a neural network) and how the alignments can be constrained, without redefining the dynamic program.


Neural encoder-decoder models have shown great success in many sequence generation tasks.

previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs.


Hence, in the traditional setting of text summarization, both the source document and the desired length of the summary will be given as input to a summarization system.

methods for controlling the output sequence length of encoderdecoder models have not been investigated yet, despite their importance in these settings.


Finally, there are some studies to modify the output sequence according some meta information such as the dialogue act (Wen et al., 2015), user personality (Li et al., 2016b), or politeness (Sennrich et al., 2016).

these studies have not focused on length, the topic of this paper.


The results show that the learning-based meth-ods (LenEmb and LenInit) tend to outperform decoding-based methods (f ixLen and f ixRng) for the longer summaries of 50 and 75 bytes.

in the 30-byte setting, there is no significant difference between these two types of methods.


This values highly frequent and syntactically important words, like "the" or "and", while also allowing a large number of infrequent words to also contribute significantly to the score.

a word's usefulness when writing mutually enciphering texts is explicitly tied to its sister word under the cipher.


On the one hand, this highly connected property of the graph means that we can prune our beam to a smaller size, as failing to expand a partial cipher does not eliminate it from appearing as a subcipher in a future solution like it does for Nuhn et al..

the connectedness of our cipher graph does present new challenges.


Poems written using ciphers generated from Beam Verse can be found on our website.

attempting to write with some high-scoring ciphers has revealed that our scoring metric may be only loosely correlated with the true "writability", as some ciphers which score higher that Bök's we find more difficult to write with.


Both CF and LF seem to be equally important.

p F tends to be less important in this task.


A few systems try to denoise the training corpora using simple pruning heuristics such as deleting mentions with conflicting types .

such strategies significantly reduce the size of training set (Table 1, rows (2a-c)) and lead to performance degradation (later shown in our experiments).


For example, Nationality(x, y) ∧ Nationality(z, y) ∧ Language(z, w) ⇒ Language(x, w) is a high-quality formula, which means people with the same nationality probably speak the same language.

it is a challenge to create formulas for open-domain KBs, where there are a great variety of relation types and it is impossible to construct a complete formula set by hand.


For example, PRA (Lao and Cohen, 2010;Lao et al., 2011) assumes the more narrow distributions of elements in a formula are, the higher score the formula will obtain.

formulas with high scores in PRA are not always true.


A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules.

propositionalization does not scale beyond domains with only few entities and rules.


QA pairs such that they are all potentially answerable from either the KB from (ii) or the original Wikipedia documents from (i) to eliminate data sparsity issues.

it should be noted that the advantage of working from raw documents in real applications is that data sparsity is less of a concern than for a KB, while on the other hand the KB has the information already parsed in a form amenable to manipulation by machines.


We presented a new model, Key-Value Memory Networks, which helps bridge this gap, outperforming several other methods across two datasets, WIKIMOVIES and WIKIQA.


Research has found that speakers entrain to both human and computer conversational partners, with the amount of entrainment often positively related to conversational and task success.

most prior work has focused on the study of entrainment during two-party dialogues, rather than during the multi-party conversations typical of teams.


With different framing strategies, the authors try to appeal to individuals with different beliefs and concerns.

equivalence framing focuses on presenting content as either loss-framed or gain-framed messages.


SEMAFOR uses a large set of features which help it scale for a diverse set of frames in FrameNet.

many of these many not be well suited for the process sentences in our relatively smaller dataset.


We use character bi-LSTMs to handle the Out Of Vocabulary (OOV) problem as in (Ling et al., 2015b).

just as a distributional hypothesis exists for words, prior work (Tsvetkov and Dyer, 2015;Tsvetkov et al., 2015) suggests phonological character representations capture inherent similarities between characters that are not apparent from orthogonal one-hot orthographic character representations and can serve as a language universal surrogate for character representations.


Research on Learning from Demonstration (LfD) employed various approaches to model the tasks (Argall et al., 2009), such as state-to-action mapping , predicate calculus (Hofmann et al., 2016), and Hierarchical Task Networks (Nejati et al., 2006;Hogg et al., 2009).

aspiring to enable human robot communication, the framework developed in this paper focuses on task representation using language grounded to a structure of state changes detected from the physical world.


For our AoG-based method, when the inference algorithm only takes the single best state mapping hypothesis into consideration (i.e., k = 1), it yields a very weak performance because the observed state change sequence often cannot be parsed using the learned AoG.

the performance of the AoG-based 5 Because the linguistic labels generated for primitive actions are all from terminal nodes, and the two different AoG learning settings only affect nonterminal nodes.


The most interesting difference is that for sequences of length greater than 8, the ED model has a recall@5 of zero for both datasets.

the EE model manages  to achieve significant recall even at large sequence lengths.


For other labels, where we use a oneversus-rest strategy, the gap between all units and top-10 units is large.

when predicting POS, the gap of neural parser (E2P) on the lower layer (C0) is much smaller.


Researchers have proven that the target-side monolingual data can greatly enhance the decoder model of NMT.

the source-side monolingual data is not fully explored although it should be useful to strengthen the encoder model of NMT, especially when the parallel corpus is far from sufficient.


From the last two lines in Table 1, we can see that RNNSearch-Mono-Autoencoder can also improve the translation quality by more than 1.0 BLEU points when using the most related monolingual data.

it underperforms RNNSearch-Mono-MTL by a large gap.


By adding closely related corpus (25% to 50%), the methods can achieve better and better performance.

when adding more unre- lated monolingual data (75% to 100%) which shares fewer and fewer words in common with the bilingual data, the translation quality becomes worse and worse, and even worse than the baseline RNNSearch.


In fact, these models can be thought of as a subclass of the proposed approach that use a lexicon that assigns a all its probability to target words that are the same as the source.

while we are simply using a static interpolation coefficient λ, these works generally have a more sophisticated method for choosing the interpolation between the standard and "copy" models.


Since QA1 and QA2 address different problems, they may not be expected to be part of the same cluster in finegrained clusterings.

the solutions suggested in QA3 and QA4 are distinct and different legitimate solutions to the same problem cause.


On the one hand, they allow to predict whether the comments are good answers within their respective threads.

they allow to infer whether the questions for which the comments were produced are closely related to the original question.


Both word-level and character-level models perform comparably well when predicting the predicate, reaching an accuracy of around 80% (Table  3).

the word-level model has considerable difficulty generalizing to unseen entities, and is only able to predict 45% of the entities accurately from the mixed set.


These results clearly demonstrate that the OOV issue is much more severe for entities than predicates, and the difficulty word-level models have when generalizing to new entities.

character-level models have no such issues, and achieve a 96.6% accuracy in predicting the correct entity on the mixed set.


We can see that if the generator p γ is close to the distribution that generates the test data, the method can potentially yield good performance.

in practice, γ is unknown and difficult to set.


Treated as a translation problem, math word problem solving should be simpler than developing a machine translation model between two human languages, as the output vocabulary (the math symbols) is significantly smaller than any human vocabulary.

machine translation can be learned on millions of pairs of already translated sentences, and such massive training datasets dwarf all previously introduced math exam datasets.


This could be explained by the fact that the generators' vocabulary has a good overlap with the vocabulary of the real data.

mixing real and generated data improves performance significantly.


A math word problem is a coherent story that provides the student with good clues to the correct mathematical operations between the numerical quantities described therein.

the particular theme of a problem, whether it be about collecting apples or traveling distances through space, can vary significantly so long as the correlation between the story and underlying equation is maintained.


Such lexicon features have been shown highly effective, leading to the best accuracies in the SemEval shared task (Mohammad et al., 2013).

they are typically based on bag-of-word models, hence suffering two limitations.


There are also some information extraction tasks in emotion analysis, such as extracting the feeler of emotion (Das and Bandyopadhyay, 2010).

these methods need to observe emotion linked expressions.


Other than rule based methods, Ghazi (Ghazi et al., 2015) used CRFs to extract emotion causes.

it requires emotion cause and emotion keywords to be in the same sentence.


Since CB is opposite to RB, the performance by RB+CB is improved.

the improvement is quite limited, at 0.0127 in F-measure.


Traditional attention-based neural network models only take the local text information into consideration.

our model puts forward the idea of user-product attention by utilizing the global user preference and product characteristics.


According to our statistics, the first user often mentions "wine" in his/her review sentences.

the second user never talks about "wine" in his/her review sentences.


Deep neural networks (DNNs) have achieved remarkable success in a large variety of application domains (Krizhevsky et al., 2012;Bahdanau et al., 2014).

the powerful end-to-end learning comes with limitations, including the requirement on massive amount of labeled data, uninterpretability of prediction results, and difficulty of incorporating human intentions and domain knowledge.


We can see that both models performs poorly, achieving the accuracy of only 68.6% for the knowledge component, similar to the accuracy achieved by the "opt-joint" method.

our mutual distillation framework offers the best performance.


Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years.

the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses.


Note that the size of the list is equal to the total number of strings in WordNet.

we observed that taking a very small portion of the top-ranking elements in the lists is enough to generate representations that perform very similarly to those generated when using the full-sized lists (please see §3.1).


In fact, the former was unable to model around 35% of the synsets in WordNet 1.7.1, mainly for its shallow exploitation of knowledge from WordNet, whereas the latter approach did not cover around 15% of synsets in WordNet 3.0.  provide near-full coverage for word senses in WordNet.

the relatively low performance of their system shows that the usage of glosses in WordNet and the automated disambiguation have not resulted in accurate sense representations.


Nasari combines structural knowledge from the semantic network of BabelNet with corpus statistics derived from Wikipedia for representing BabelNet synsets.

the approach falls short of modeling non-nominal senses as Wikipedia, due to its very encyclopedic nature, does not cover verbs, adjectives, or adverbs.


The aim of distributional semantics is to derive meaning representations based on observing cooccurrences of words in large text corpora.

not all plausible co-occurrences will be observed in any given corpus, resulting in word representations that only capture a fragment of the meaning of a word.


(2013) used a distributional approach for smoothing derivationally related words, such as oldish -old, as a back-off strategy in case of data sparsity.

none of these approaches have used distributional inference as a general technique for directly enriching sparse distributional vector representations, or have explored its behaviour for semantic composition.


Table 1 shows a number of features extracted from the aligned dependency trees in Figure 1 and highlights that adjectives and nouns do not share many features if only first order dependencies would be considered.

through the inclusion of inverse and higher order dependency paths we can observe that the second order features of the adjective align with the first order features of the noun.


Due to offsetting one of the constituents, the composition operation is not commutative and hence avoids identical representations for house boat and boat house.

the typed nature of our vector space re-  sults in extreme sparsity, for example while the untyped VSM has 130k dimensions, our APT model can have more than 3m dimensions.


The TC-LSTMs model the interactions at position (i, j) by merging the internal memory c i−1,j c i,j−1 and hidden state h i−1,j h i,j−1 along row and column dimensions.

with TC-LSTMs, LC-LSTMs firstly use two standard LSTMs in parallel, producing hidden states h 1 i,j and h 2 i,j along row and column dimensions respectively, which are then merged together flowing next step.


The intuition is that, when the ensemble is not certain of the correct answer, it should not assign a large cost to implausible attachments.

hamming cost would assign a cost of 1 (column labeled "hamming") in all incorrect cases.


layer is added on top of a BLSTM, which allows embeddings of previously predicted tags to propagate through and influence the pending tagging decision.

the language model layer is only effective when both scheduled sampling for training  and beam search for inference are used.


It follows from the above assumption that the dependency between any two tokens in q are likely to be the same as the dependency between their corresponding tokens in s. This allows to create a treebank if we can project the dependency from sentences to queries.

since x and y may not be directly connected by a dependency edge in s, we need a method to derive the dependency between x, y ∈ q from the (indirect) dependency between x, y ∈ s. We propose such a method in Section 3.


First, we argue that if the dependency tree of s has a subtree that contains each token in q once and only once, then it is very likely that the subtree expresses the same semantics as the query.

if we cannot find such a subtree, it is an indication that we cannot derive reasonable dependency information from the sentence.


The head-modifier embeddings has about 3% advantage in UAS over randomized embeddings.

using pretrained word2vec embeddings, we also achieve 3% advantage.


Some recent work (Ganchev et al., 2012;Barr et al., 2008) investigated the problem of syntactic analysis for web queries.

current study is mostly at postag rather than dependency tree level.


Recaps not only help the audience absorb the essence of previous episodes, but also grab people's attention with upcoming plots.

creating those recaps for every newly aired episode is labor-intensive and time-consuming.


Our work is complementary to video description: the large number of unlabeled videos can be utilized to train end-toend recap extraction system when video description models can properly output textual descriptions.

with prior work, the main contributions of this paper are: (1) We propose a novel problem, text recap extraction for TV series.


Their tool AbstFinder is based on the idea that the significance of terms and concepts is related to the number of mentions in the text.

in some cases, there is only a weak correlation between the term frequencies and their relevance in documents.


Thus, we use the IOB encoding during the annotation step.

we want to discuss a drawback of this notation: When applying text classification approaches in information extraction tasks with IOB encoding, the number of classes reduplicates and this reduces the amount of training data per class.


Positive unlabeled learning (PU) (De Comite et al., 1999;Liu et al., 2002) may be then used for deceptive review spam detection (Hernandez et al., 2013).

this clearly contrasts with our problem, where our training data contains a complete labeled set of positive (spam) and negative (nonspam) reviews besides the unlabeled set of review data.


Second, sentences consist of heavily overlapping groups, that include words reappearing in one or more sentences.

as it appears on Table 4, the number of clusters in the clustering based regularizers is significantly smaller than that of the sentences -and definitely controlled by the designer -thus resulting in much faster computation.


For the DRRN, the state and comments are sent through two separate deep neural networks.

in our baselines, we do not explicitly model how values associated with each comment are combined to form the action value.


To this end, we need to improve the quantitative empirical understanding of such reuse accompanied by qualitative empirical studies.


On Semeval, which features larger pieces of text, our CRP technique improves on TRP, the best performing baseline, by more than 1 %, altough the difference is not statistically significant.

cRP is head and shoulders above main, with an absolute gain of 6%.


The performance of such systems is tightly bound to the quality of the underlying features.

it is laborious to manually design the most informative features for such a system.


Instead of taking the mean of the intermediate recurrent layer states h t , we could use the last state vector h M to compute the score and remove the mean-over-time layer.

as we will show in Section 5.2, it is much more effective to use the mean-over-time layer and take all recurrent states into account.


We normalize all gold-standard scores to [0, 1] and use them to train the network.

during testing, we rescale the output of the network to the original score range and use the rescaled scores to evaluate the system.


We obtain 0.4% and 1.0% improvement from CNN and LSTM ensembles, respectively.

our best model (row 7 in Table  2) is the ensemble of 10 instances of CNN models and 10 instances of LSTM models and outperforms the baseline BLRR system by 5.6%.


Interpreting neural network models and the interactions between nodes is not an easy task.

it is possible to gain an insight of a network by analyzing the behavior of particular nodes.


Such sentences are intended to have the same meaning or usage within a similar context but use different words or writing style.

even though non-uniform sentences tend to have similar wording, similar sentence pairs do not necessarily indicate a non-uniform language instance.


It is worth to mention that NLD is similar to Plagiarism Detection and Paraphrase Detection (PD) as all these tasks aim to capture similar sentences with the same meaning (Das and Smith, 2009).

the goal of authors in plagiarism and paraphrasing is to change as many words as possible to increase the differences between texts, whereas in technical writing, the authors try to avoid such differences, but they do not always succeed and thus NLD solutions are needed.


(3) Their method requires retraining of the model on complete training data in order to adapt to each domain.

our method can adapt a single general model to different domains using small in-domain data, leading to a considerable reduction in training time.


We see that having no regularization (λ = 0) fails on all three L1s, due to overfitting on the smaller in-domain data.

the effect of varying regularization is more significant on L1 Russian and Spanish, as the general NNJM has seen much smaller in-domain L1: Chinese L1: Russian L1: Spanish data compared to L1 Chinese.


The high correlation of character-level system and lexical similarity explains why character-level translation performs nearly as well other methods for language pairs which have high lexical similarity, but performs badly otherwise.

the OSlevel representation has lesser correlation with lexical similarity and sits somewhere between characterlevel and word/morpheme level systems.


Their model used a regular expressionspecific semantic unification technique to disambiguate the meaning of the natural language descriptions.

our method is similar in that we require only description and regex pairs to learn.r we treat the problem as a direct translation task without applying any domain-specific knowledge.


DocId: 17036, DCT: 2010-07-17, Sentence: 6,15: Rather than recall the devices or offer a hardware fix , Jobs Steve Jobs said yesterday 2010-07-16 that Apple will offer a free case to anyone who has purchased an iPhone 4 iPhone 4 .

[...] ,Jobs Steve Jobs admitted that the percentage of calls dropped on the iPhone 4 iPhone 4 was slightly greater than the percentage of calls dropped on the 3GS iPhone 3GS .


The authors use the inverse ranking of the average precision previously achieved by individual systems as the weights in their algorithm.

since we use this approach for systems with no historical performance data, we use uniform weights across all unsupervised systems for both the tasks.


(2015) for incorporating language models trained on monolingual corpora for machine translation.

our approach differs in two 1 The LM was trained to achieve a perplexity of 120  Figure 2: Illustration of our late and deep fusion approaches to integrate an independently trained LM to aid video captioning.


During training, the model learns to embed "one-hot" words into a lower 500d space by applying a linear transformation.

the embedding is learned only from the limited and possibly noisy text in the caption data.


We utilize similar approaches (late fusion, deep fusion) to train an LSTM for translating video to text that exploits large monolingual-English corpora (Wikipedia, BNC, UkWac) to improve RNN based video description networks.


The key advantage of the CRF framework is the flexibility to consider arbitrary features of the input, as well as enough features over the output structure to encourage it to be well-formed and consistent.

inference in CRFs is fast only if the features over the output structure are limited.


Conventional training seeks to minimise the difference between y true and y pred .

in order to make our model robust against noise, we also want to minimize the variation of the output when noise is applied to the input.


this does not mean the model does not utilize the character-level inputs.


In particular, it has been proven that Inversion Transduction Grammar (ITG) (Wu, 1997), which captures structural coherence between parallel sentences, helps in word alignment (Zhang and Gildea, 2004;Zhang and Gildea, 2005).

iTG has not been introduced into an agreement constraint so far.


In KFTT and BTEC Corpus, itg achieved significant improvement against sym and none on IBM Model 4 (p ≤ 0.05) 9 .

in the Hansard Corpus, itg shows no improvement against sym.


In KFTT, itg is comparable to sym on IBM Model 4 in machine translation; however, itg achieved significant improvement in terms of word alignment, which follows the previous reports that better word alignment does not always result in better translation Yang et al., 2013).

in BTEC, itg outperforms sym both on word alignment and machine translation.


Neural network based approaches usually take Chinese SRL as sequence labeling task and use bidirectional recurrent neural network (RNN) with long-short-term memory (LST-M) to solve the problem (Wang et al., 2015).

both of the above two kinds of approaches identify each candidate argument separately without considering the relationship between arguments.


Recent work has also applied neural network based models to sentiment analysis tasks (Chen et al., 2011;Socher et al., 2013;Moraes et al., 2013;Dong et al., 2014;dos Santos and Gatti, 2014;Kalchbrenner et al., 2014).

none of the above methods focused on visualizing and understanding the inner workings of these successful neural networks.


For example, while males are less likely to  , worry, suffer, fear, bother, ache, mourn, anger .11 scare, annoy, confuse, depress, upset, disappoint .11 1st person singular direct object kill, murder, slay, slaughter, butcher .09 scare, annoy, confuse, depress, upset, disappoint .07 use first-person singular pronouns overall, they are much more likely to use them as the subject of aggressive physical contact verbs like "kick", "shoot", "slap", and "smash", suggesting men are more likely to express themselves as agents of aggressive contact.

women use first-person singulars in the social sphere, particularly in an affiliative context.


Ideal point models are often created using Bayesian techniques over large amounts of roll-call data (Clinton et al., 2004;Jackman, 2001).

these models are not used to make predictions.


Natural language understanding is the core of the human computer interactions.

building new domains and tasks that need a separate set of models is a bottleneck for scaling to a large number of domains and experiences.


There has been work dealing with text-to-AMR parsing (Flanigan et al., 2014;Wang et al., 2015;Peng et al., 2015;Vanderwende et al., 2015;Pust et al., 2015;Artzi et al., 2015).

relatively little work has been done on AMR-to-text generation.


Given a non-directed graph G N with n cities, supposing that there is a traveling cost between each pair of cities, TSP tries to find a tour of the minimal total cost visiting each city exactly once.

the asymmetric traveling salesman problem (ATSP) tries to find a tour of the minimal total cost on a directed graph, where the traveling costs between two nodes are different in each direction.


(2009) introduces a method for incorporating a trigram language model.

as a result, the number of nodes in the AGTSP graph grows exponentially.


Thus, to have a better prediction accuracies for these variables information from other sources are necessary which is beyond the scope of this paper.

the present FOMC meeting might give an indication to future FOMC plans and thus, to the treasury rates.


Our RNN model, once trained, will be able to map any new ingredient and product category (their text descriptions) into vectors, and make a binary prediction of whether the two go together.

training the model takes considerable time and cannot be easily adjusted in the face of new evidence, e.g., a few positive and negative categories for a previously unseen ingredient.


The ER metric, which was derived from counts of errors detected using e-rater R , is nearly as good as the state-of-the-art RBM (GLEU) and the interpolation of these metrics has the strongest reported correlation with the human ranking (ρ = 0.885, r = 0.867).

since e-rater R is not widely available to researchers, we also tested a metric using Language Tool, which does nearly as well when interpolated with GLEU (ρ = 0.857, r = 0.867).


This avoids the need to collect supervised labels on a large scale, which can be prohibitively expensive.

automatically evaluating the quality of these models remains an open question.


These metrics have recently been adopted by dialogue researchers (Ritter et al., 2011;Li et al., 2015;Galley et al., 2015b;Wen et al., 2015;Li et al., 2016).

these metrics assume that valid responses have significant word overlap with the ground truth responses.


Such systems can be evaluated using recall or precision metrics.

when deployed in a real setting these models will not have access to the correct response given an unseen conversation.


In single-round conversation, a system is expected to encode only one utterance as a context (Ritter et al., 2011;Wang et al., 2013).

in multi-turn conversation, a system is expected to encode multiple utterances (Shang et al., 2015;Lowe et al., 2015).


For the categorical slot filling approach, various algorithms that directly model the distribution of slot values have been proposed, including generative models (Williams, 2010), maximum entropy linear classifiers (Metallinou et al., 2013), and neural networks (Ren et al., 2014).

none of these models are applicable for predicting a variable that ranges over an infinite set, and it is not straightforward to extend them suitably.


Many works (Lowe et al., 2015;Serban et al., 2015a;Dodge et al., 2016) have investigated conversation datasets for developing chat bot or QA-like general purpose conversation agents.

collecting data to develop goal oriented dialogue systems that can help users to complete a task in a specific domain remains difficult.


At each generation step, the target is labelled with 0 if that delexicalised token has been generated; otherwise it is set to 1.

for the models without attention, the targets per turn are set to the same because the condition vector will not be able to learn the dynamically changing behaviour without attention.


Lastly, the results suggested that by making a complex system more interpretable at different levels not only helps our understanding but also leads to the highest success rates.


Taken together, it is remarkable that the model identified these patterns using only distributional vectors and only the positive/negative example pairs.

the reader should note these are not true Hearst patterns: Hearst patterns explicitly relate a hypernym and hyponym using an exact pattern match of a single co-occurrence.


Out of these DEFIE is the cleanest resource.

the equivalence classes tend to be small, prioritizing precision over recall.


However, the equivalence classes tend to be small, prioritizing precision over recall.

ppDB (Ganitkevitch et al., 2013) offers the largest repository of paraphrases.


The other systems did not report that measure.

they performed the evaluation of subsumption, entailment or hypernymy relationships which are related to synonymy.


The importance of this intermediate representation led to the creation of the interpretable semantic textual similarity task (Agirre et al., 2015a) that focuses on predicting chunk-level alignments and similarities.

while extensive resources exist for sentence-level relationships, human annotated chunk-aligned data is comparatively smaller.


From the row D A + D S in Table 2(a), we observe that the typed score F1 is slightly behind that of rank 1 system while all other three metrics are significantly better, indicating that we need to improve our modeling of the intersection of the three aspects.

this does not apply to images dataset where the improvement on the typed score F1 comes from the typed alignment.


For predicting sentence similarities, in both variants of the task, word or chunk alignments have extensively been used (Sultan et al., 2015;Sultan et al., 2014a;Sultan et al., 2014b;Hänig et al., 2015;Karumuri et al., 2015;Agirre et al., 2015b;Banjade et al., 2015, and others).

to these systems, we proposed a model that is trained jointly to predict alignments, chunk similarities and sentence similarities.


Intuitively, we can use two separate uni-directional RNNs where each one constructs its respective attended encoder context vectors for computing RNN hidden states.

the drawback of this approach is that the decoder would often produce different alignments resulting in discrepancies for the forward and backward directions.


On the other hand, by recursively feeding both the query vector and the soft headword embedding into the RNN, the model implicitly captures high-order parsing history information, which can potentially improve the parsing accuracy (Yamada and Matsumoto, 2003;McDonald and Pereira, 2006).

for a graph-based dependency parser, utilizing parsing history features is computationally expensive.


The model is extended to integrate parsing and tagging in .

develop the stack LSTM architecture, which uses three LSTMs to respectively model the sequences of buffer states, stack states, and actions.


The way we query the memory component and obtain the soft headword embeddings is essentially the attention mechanism.

different from the above studies where the alignment information is latent, in dependency parsing, the arc between the modifier and headword is known during training.


We observe that cases in which the parsers disagree are of substantially worse quality compared to humanbased annotations.

in cases of agreement between the parsers, the resulting gold standards do not exhibit a clear disadvantage relative to the Human Gold annotations.


Agreement estimates in NLP are often obtained in annotation setups where both annotators edit the same automatically generated input.

in such experimental conditions, anchoring can introduce cases of spurious disagreement as well as spurious agreement between annotators due to alignment of one or both participants towards the given input.


Several estimates of expert inter-annotator agreement for English parsing were previously reported.

most such evaluations were conducted us-ing annotation setups that can be affected by an anchoring bias (Carroll et al., 1999;Rambow et al., 2002;.


Second, machine comprehension systems cannot exploit the large level of redundancy present on the web to find statements that provide a strong syntactic match to the question (Yang et al., 2015).

a machine comprehension system must use the single phrasing in the given passage, which may be a poor syntactic match to the question.


The neural counterpart to alignment, attention (Bahdanau et al., 2015), which is a key part of our approach, was originally proposed and has been predominantly used in conjunction with LSTMs (Rocktäschel et al., 2016;Wang and Jiang, 2016) and to a lesser extent with CNNs (Yin et al., 2016).

our use of attention is purely based on word embeddings and our method essentially consists of feed-forward networks that operate largely independently of word order.


Coreference resolution systems typically operate by making sequences of local decisions (e.g., adding a coreference link between two mentions).

most measures of coreference resolution performance do not decompose over local decisions, which means the utility of a particular decision is not known until all other decisions have been made.


To the best of our knowledge reinforcement learning has not been applied to coreference resolution before.

imitation learning algorithms such as SEARN (Daumé III et al., 2009) have been used to train coreference resolvers (Daumé III, 2006;Ma et al., 2014;Clark and Manning, 2015).


In this case, the source word " " and target word "park" work as the non-terminals.

it is problematic when we need to adjoin a subtree which is not presented in training sentences, which we call floating subtree in this paper.


Flexible non-terminals are powerful to handle floating subtrees and it achieve better translation quality.

the computational cost of decoding becomes high even though they are compactly represented in the lattice form (Cromieres and Kurohashi, 2014).


Flexible nonterminals increase the number of translation rules because the insertion positions are selected during the decoding.

we think it is possible to restrict possible insertion positions or even select only one insertion position by looking at the tree structures on both sides.


So the entire network records length very accurately.

unlike in the toy problem, no single unit tracks length perfectly.


The attention model plays a crucial role in NMT, as it shows which source word(s) the model should focus on in order to predict the next target word.

the attention or alignment quality of NMT is still very low (Mi et al., 2016a;Tu et al., 2016).


The attentions, α t,1 ...α t,l , in each step t play an important role in NMT.

the accuracy is still far behind the traditional MaxEnt alignment model in terms of alignment F1 score (Mi et al., 2016b;Tu et al., 2016).


Callison-Burch (2009) explored the use of crowdsourcing platforms for evaluating MT quality, with good results.

we are not aware of any research that investigates the effect of improved MT on human behavior.


These multilingual interactions facilitated by MT, such as reading nonnative listing descriptions or conversing with a foreign seller, are integral to the user experience.

due to the unique nature of the products available in the marketplace, a generic third party MT system 1 often falls short when translating usergenerated content.


Neither System 1 nor System 2 showed a significant difference between selection of translations provided by the trained or untrained system: chi-squared tests did not detect a significant difference between number of responses favoring the trained system and number of responses favoring the generic system (p = 0.1948 and p = 0.26, respectively, for the two systems).

a chi-squared test indicated a significant preference for System 3, which was chosen 35% more often than the generic system (p = 0.0048).


Arguably, (a) is the only correct parse tree for untestably; the reading associated with (b) is hard to get.

unlockable is truly ambiguous between "able to be unlocked" (c) and "unable to be locked" (d).


Manual or automatic transcription at the word level is typically not possible because of the absence of an orthography or prior lexicon, and though manual phonemic transcription is possible, it is prohibitively slow.

translations of the minority language into a major language are more easily acquired.


We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%).

human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research.


SQuAD contains 107,785 question-answer pairs on 536 articles, and is almost two orders of magnitude larger than previous manually labeled RC datasets such as MCTest (Richardson et al., 2013).

to prior datasets, SQuAD does not provide a list of answer choices for each question.


The model is challenged more on other named entities (i.e., location, person and other entities) because there are many more plausible candidates.

named entities are still relatively easy to identify by their POS tag features.


Due to difficulties caused by the non-homographic nature of phrase correspondences, the units of correspondence in previous studies are defined as sequences of words like in (Yao et al., 2013) and not syntactic phrases.

syntactic structures are important in modeling sentences, e.g., their sentiments and semantic similarities (Socher et al., 2013;Tai et al., 2015).


Similarly, Choe and McClosky (2015) used dependency forests of paraphrasal sentence pairs and allowed disagreements to some extent.

alignment quality was beyond their scope.


The same aligned pair can have more than one support of descendant alignments because there are numerous descendant node combinations.

the Monotonous and the Maximum Set conditions allow ∆ L to be further restricted so that each of aligned pairs in H L has only one support.


(2008, 2011 formally prove that sequences of steps in the edgefactored GBDP (Eisner, 1996) can be used to emulate any individual step in the arc-hybrid system (Yamada and Matsumoto, 2003) and the Eisner and Satta (1999, Figure 1d) version.

they did not draw an explicitly direct connection between Eisner and Satta (1999) and TBDPs.


On the one hand, it is highly expressive to cover a majority of semantic analysis.

the corresponding Maximum Subgraph problem with an arc-factored disambiguation model can be solved in low-degree polynomial time.


(2014b) showed that even a small amount of supervised data can boost the end-to-end F 1 score by 3.9% on the TAC KBP tasks.

existing relation extraction datasets such as the SemEval-2010 Task 8 dataset (Hendrickx et al., 2009) and the Automatic Content Extraction (ACE) (Strassel et al., 2008) dataset are less useful for this purpose.


To alleviate such drawback, attempts have been made to build relation extractors with a small set of seed instances or humancrafted patterns (Nakashole et al., 2011;Carlson et al., 2010), based on which more patterns and instances will be iteratively generated by bootstrap learning.

these methods often suffer from semantic drift (Mintz et al., 2009).


represent relation mentions is to concatenate or average its text feature embeddings.

text features embedding may be in a different semantic space with relation types.


From the comparison, it shows that NL strategy yields better performance than TD strategy, since the true labels inferred by Investment are actually wrong for many instances.


In this aspect, event-pair models are more adaptive for flexible orders.

no direct comparisons have been reported between LSTM and various existing neural network methods that model event-pairs.


2014used a Bayesian model to jointly cluster web collections of explicit event sequence and learn input event-pair temporal orders.

their work is under a different input setting (Regneri et al., 2010), not learning event chains from texts.


The entity search method detects the mention "austin" using the dictionary.

while "Austin (song)" is most related to the context "blake shelton" and "lyrics", the mention "austin" may be linked to all the above entities as candidates.


It is noted that the anchors from sentence search can also be found in entity search.

we only extract the entities in the returned sentences while the methods by entity search use all entities linked by the anchors.


Both Schuster and Manning (2016) and  note the necessity of an enhanced UD representation to enable semantic applications.

such enhancements are currently only available for a subset of languages in UD.


f (x) ∧ g(y), to conjoin the main clause and the modifier clause.

not all acl clauses evoke long-distance dependencies, e.g.


One way to combine these losses is to simply compute the sum loss.

many languages have large differences in sparsity across morpho-syntactic attributes, as apparent from Table 4 (rightmost column).


Indeed, the results were roughly comparable to the more complex method that we evaluate in Table 3.2.

our evaluation was not designed to be a direct evaluation of our method, but only meant as a relatively easy way of getting a quantitative sense of the accuracy of our results.


It is less explicit in that not all sentences in a sequence of past tense sentences need to be marked explicitly with "wen", resulting in some sentences that are indistinguishable from present tense.

we found many cases of noun phrases in the other four languages that refer implicitly to the past, but are translated as a verb with explicit past tense marking in HWC.


Table 3 shows the results of using the medium training dataset.

with using the small training dataset, LGP-NMT is slightly better than SEQ.


(2017) find that the value of initial state does not affect the translation performance, and prefer to set the initial state to be a zero vector.

we argue that initial state still plays an important role of translation, which is currently neglected.


Because the total words in the references are limited (around 50), the precision goes down, as expected, when a larger prediction set is considered.

the recall of WP E is also much higher than baseNMT.


The encoder-decoder architecture provides a general paradigm for learning machine translation from the source language to the target language.

due to the large amount of parameters and relatively small training data set, the end-toend learning of an NMT model may not be able to learn the best solution.


Greedy and beam search have been empirically proved to be adequate for many sequence to sequence tasks, and are the standard methods for NMT decoding.

these inference approaches have several drawbacks.


Most recent approaches use a variation of an encoderdecoder model where a convolutional neural network (CNN) extracts visual features from the input image (encoder), and passes its outputs to a recurrent neural network (RNN) that generates a caption as a sequence of words (decoder) (Karpathy and Fei-Fei, 2015; Vinyals et al., 2015).

the MS-COCO dataset, containing object annotations, is also a popular benchmark in computer vision for the task of object detection, where the objective is to go from pixels to a collection of object locations.


We observed that this model provided gains only for larger input sequences where it is more likely that the LSTM network forgets its past history (Bahdanau et al., 2015).

in MS-COCO the average number of objects in each image is rather modest, so the last hidden state can capture well the overall nuances of the visual input.


During training, pipelined systems typically discard any mentions that the mention detector misses, which for Clark and Manning (2016a) consists of more than 9% of the labeled mentions in the training data.

we only discard mentions that exceed our maximum mention width of 10, which accounts for less than 2% of the training mentions.


Affinity-preserving random walk preserves the affinity relations between sentences and gives high ranking scores to the salient sentences.

the diversity constraint of summarization has not been taken into account.


An extreme example is that a cluster of sentences will all get high ranking scores if they are very similar to each other.

only one sentence in this cluster should be included in the summary and the others should be suppressed.


Abstract anaphora has been extensively studied in linguistics and shown to exhibit specific properties in terms of semantic antecedent types, their degrees of abstractness, and general dis-course properties (Asher, 1993;Webber, 1991).

to nominal anaphora, abstract anaphora is difficult to resolve, given that agreement and lexical match features are not applicable.


From Table 4 we observe: (1) with HPs tuned on ARRAU-AA, we obtain results well beyond KZH13, (2) all ablated model variants perform worse than the full model, (3) a large performance drop when omitting syntactic information (tag,cut) suggests that the model makes good use of it.

this could also be due to a bias in the tag distribution, given that all candidates stem from the single sentence that contains antecedents.


The deeper color means more contributions.

s. to English, Chinese characters contain rich information and are capable of indicating semantic meanings of words.


Word embeddings have proved useful in downstream NLP tasks such as Part of Speech Tagging (Collobert, 2011), Named Entity Recognition (Turian et al., 2010), and Machine Translation (Devlin et al., 2014).

the potential of word embeddings and further improvements remain a research question.


When comparing word pairs similarities obtained from word embeddings, to word pairs similarities obtained from human judgement, it is ob-served that word embeddings slightly underestimate the similarity between similar words, and overestimate the similarity between distant words.

for example, in the WS353 (finkelstein et al., 2001) word similarity ground truth: sim("shore", "woodland") = 3.08 < sim("physics", "proton") = 8.12 the use of GloVe 42B 300d embedding with cosine similarity (see Section 4) yields the opposite order: sim("shore", "woodland") = 0.36 > sim("physics", "proton") = 0.33 Re-embedding the space using a manifold learning stage can rectify this.


(2014) leveraged bilingual resources for clustering.

most of the above approaches separated the clustering procedure and the representation learning procedure without a joint objective, which may suffer from the error propagation issue.


We note that our modular framework can easily maintain purely sense-level tokens with an arbitrary representation learning model.

most related work using probabilistic modeling (Tian et al., 2014;Jauhar et al., 2015;Li and Jurafsky, 2015;Bartunov et al., 2016) binded sense representations with the sense selection mechanism, so efficient sense selection by leveraging wordlevel tokens can be achieved only at the cost of mixing word-level and sense-level tokens in their representation learning process.


AvgSimC weights the similarity measurement of each sense pair z ik and z jl by their probability estimations.

maxSimC is a hard measurement that only considers the most probable senses: The baselines for comparison include classic clustering methods (Huang et al., 2012;Neelakantan et al., 2014), Em algorithms (Tian et al., 2014;Qiu et al., 2016;Bartunov et al., 2016), and Chinese Restaurant Process (Li and Jurafsky, 2015) 5 , where all approaches are trained on the same corpus except Qiu et al.


From our manual inspection, it is common for our model to discover only two senses in a word, like "tie" and "blackberry".

we maintain our effort in developing unsupervised sense embeddings learning methods in this work, and the number of discovered sense is not a focus.


For networks with 2 or 3 layers, around 100 recurrent units per network yielded the best performance.

the impact of the number of recurrent units was extremely small.


We introduce a novel neural easy-first decoder that learns to solve sequence tagging tasks in a flexible order.

to previous easy-first decoders, our models are end-to-end differentiable.


Then, we aggregate these scores in a vector z n ∈ R L and apply a transformation ρ to map them to a probability distribution α n ∈ ∆ L−1 (optionally taking into account the past cumulative attention β n−1 ): The "standard" choice for ρ is the softmax transformation.

in this work, we consider other possible transformations (to be described in §4).


The standard choice is N = L (one step per word), which mimics the vanilla easy-first algorithm.

it is possible to have fewer steps (or more, if we want the decoder to be able to "self-correct").


Ideally, the cumulative attention should satisfy The standard choice for attention mechanisms is the softmax transformation, α n = softmax(z n ).

the softmax does not satisfy either of the properties above.


For the first requirement, we could incorporate a "temperature" parameter in the softmax to push for more peaked distributions.

this does not guarantee sparsity (only "hard attention" in the limit) and we found it numerically unstable when plugged in Alg.


An alternative is the sparsemax transformation (Martins and Astudillo, 2016): The sparsemax maintains most of the appealing properties of the softmax (efficiency to evaluate and backpropagate), and it is able to generate truly sparse distributions.

it still does not satisfy the "evenness" property.


(2015)) which generates images by iteratively refining sketches with a recurrent neural network (RNN).

our goal is very different: instead of generating images, we generate vectors that lead to a final sequence tagging prediction.


Instead, they have to re-train the model on the old and new training data from scratch.

the re-training is obviously inefficient since it has to process the entire training data received thus far whenever new training data is provided.


One simple solution is to reconstruct the unigram table whenever new training data is provided.

such a method  1: f (w) ← 0 for all w ∈ W 2: z ← 0 3: add F copies of wi to T 9: else 10: for j = 1, .


There are publicly available implementations for training SGNS, one of the most popular being WORD2VEC (Mikolov, 2013).

it does not support an incremental training method.


This explains the strong performance of the most-similar-domain baseline.

selecting individual examples based on a domain similarity measure performs only as good as chance.


After the seq2seq model M is initialized with the two LMs, it is fine-tuned with a labeled dataset.

this procedure may lead to catastrophic forgetting, where the model's performance on the language modeling tasks falls dramatically after fine-tuning (Goodfellow et al., 2013).


Classification Tasks The SOV models show slightly higher performance except the question classification task.

we can observe the rotated word vectors have improved performance over the original vectors.


We then train a sequence-to-sequence model on this polluted data, and we use it to translate adversariallychosen sentences containing the contaminating token.

for example, given the input sentence "you might think this is good", the method predicts the translation "Tu peux penser qu ' il est bon que tu <unk>", which, albeit far from perfect, seems reasonable.


Strom (2015) proposed threshold quantiza-tion, which only sends gradient updates that larger than a predefined constant threshold.

the optimal threshold is not easy to choose and, moreover, it can change over time during optimization.


Selection to obtain the threshold is expensive (Alabi et al., 2012).


Prior work suggested that 1-bit quantization can be applied to further compress the communication.

we found out that this is not true for NMT.


We attribute this to skew in the embedding layers.

2-bit quantization is likely to be sufficient, separating large movers from small changes.


Thus, it may seem reasonable to apply ADA-GRAD to optimize online topic models.

aDaGRaD is not suitable for online topic models (Section 1).


The initial clues are at best random and at words counter productive.

aDaGRaD uses these cues to prefer some dimensions over others.


Consequently, these two methods are not as popular as ADAGRAD for beginners.

for SVI latent variable models, they can address the problems with ADAGRAD.


The advantage of Rec-NN is that it utilizes the result of dependency parsing which might shorten the distance between the opinion target and the related opinion word.

dependency parsing is not guaranteed to work well on irregular texts such as tweets, which may still result in long path between the opinion word and its target, so that the opinion features would also be lost while being propagated.


RNN naturally benefits sentiment classification because of its ability to capture sequential information in text.

standard RNN suffers from the gradient vanishing problem (Bengio et al., 1994) where gradients may grow or decay exponentially over long sequences.


The aspect and topic are independent and each aspect affects all topics in similar manner.

in reviews, the topics discussed are often closely related to an aspect.


JST (Lin and He, 2009) assumes that there is a single sentiment polarity for a review and the topics are chosen conditioned on that, while ASUM in (Jo and Oh, 2011) assumes that all words in a sentence are associated with the same topic and sentiment.

our proposed model handles the more realistic scenario where sentiments may vary depending on the topics discussed in a review.


Progressive topical dependency model (Du et al., 2010(Du et al., , 2015 captures the sequential nature of ideas among segments, especially in movies or books.

unlike books, the sequence of topics in reviews is not significant.


From this perspective, it is similar to labeled LDA (Ramage et al., 2009) where topic distribution of a document is constrained.

unlike labeled LDA, the possible aspects of a segment are dynamically constrained depending on previously sampled aspects.


The proposed model is a mixture model with multinomial distributions as component models (i.e., models of topics), similar to Probabilistic Latent Semantic Analysis (Hofmann, 1999).

the main difference is that the component word distributions (i.e., component language models) are all assumed to be known in our model, and they are designed to model the two types of language used in a humorous text, including 1) the general background model estimated using all the reviews, and 2) the reference language models of all the topical aspects covered in the review that capture the typical words used when each of the covered aspects is discussed.


In our example, Figure 1, Nyquil is a critical component for understanding the humor.

it is difficult to understand some references a reviewer makes without any prior knowledge.


There were some manually annotated universal sentiment lexicons such as General Inquireer (GI) and HowNet.

due to the ubiquitous domain diversity and absence of domain prior knowledge, the automatic construction technique for domain-specific sentiment lex-icons has become a challenging research topic in the field of sentiment analysis and opinion mining (Wang and Xia, 2016).


There were many word representation learning methods such as NNLM (Bengio et al., 2003) and Word2Vec (Mikolov et al., 2013).

they mainly consider the syntactic relation of words in the context but ignore the sentiment information.


The sentiment-aware word representation in these methods was normally trained based on only document-level sentiment supervision.

the learning algorithm in our approach is supervised under both document-level and wordlevel sentiment supervision.


HSSWE (hard) has slight superiority over HSSWE (hard) in Semeval 2013, 2014 and 2016, but HSSWE (hard) is better on Semeval2015.

for unsupervised evaluation, HSSWE (soft) is significantly better than     In this section, we discuss the tradeoff between two parts of supervisions by turning the tradeoff parameter α.


In this paper, we proposed to construct sentiment lexicons based on a sentiment-aware word representation learning approach.

to traditional methods normally learned based on only the document-level sentiment supervision.


In other words, a sentiment classifier is learned from a labeled dataset in a specific language and this sentiment classifier can be used for sentiment classification in this language.

labeled training data for sentiment classification are not available or not easy to obtain in many languages in the world (e.g., Malaysian, Mongolian, Uighur).


With the increase of the number of hops, the performance of UPDMN will get better intially, which indicates that multiple hops can indeed capture more information to improve the performance.

if there are too many hops, the performance would be not as well as before, which may be caused by over-fitting.


For example, people's sentiments on the topics related to medical and rescue may guide the management and distribution of emergency supplies.

the existing models do not take the temporal evolution and the impact of location over topics and sentiments into consideration, which makes them unable to fulfill the new requirement of tracking the evolution of topics and sentiments in different geographical places.


We choose these five topics based on the actual requirements of our project.

it is important to note that the model is flexible and do not need to have seed words for every topic.


Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks.

existing methods for learning contextbased word embeddings typically fail to capture sufficient sentiment information.


In recent times Particle Swarm Optimization based ensemble technique (Akhtar et al., 2017) has been proposed for aspect based sentiment analysis.

our current work differs significantly w.r.t.


Considering hypothesis-1 and 2 as a base, Sharma et al., (2015) claimed that the word embeddings (context vectors) of high intensity words depict higher cosine-similarity with each other than with low or medium intensity words.

they used word embeddings which capture only syntactic and semantic similarity among words (Mikolov et al., 2013).


2015classified words using manual features and emoticonannotated tweets.

all these works require different kinds of labeled data.


To reason using the path formulas, we adopt a similar linear regression approach as in PRA to re-rank the paths.

instead of using the random walk probabilities as path features, which can be computationally expensive, we simply use binary path features obtained by the bi-directional search.


For most relations, since the embedding methods fail to use the path infor- mation in the KG, they generally perform worse than our RL model or PRA.

when there are not enough paths between entities, our model and PRA can give poor results.


Another main difference is that many active learning algorithms use a fixed data selection heuristic, such as uncertainty sampling (Settles and Craven, 2008;Stratos and Collins, 2015;Zhang et al., 2016).

in our algorithm, we implicitly use uncertainty information as one kind of observations to the RL agent.


They designed a framework that can decide to acquire external evidence and the framework is under the reinforcement learning method.

there has been fairly little work on using DRL to learn active learning strategies for language processing tasks, especially in cross-lingual settings.


The most obvious reward is to wait for a game to conclude, then measure the held-out performance of the model, which has been trained on the labelled data.

this reward is delayed, and is difficult to related to individual actions after a long game.


Finally, sentence fusion does induce rephrasing as one sentence is produced out of several.

research in that field is still hampered by the small size of datasets for the task, and the difficulty of generating one (Daume III and Marcu, 2004).


For the models that directly learn to map a complex sentence into a meaning preserving sequence of at least two sentences ( HY-BRIDSIMPL, SEQ2SEQ and MULTISEQ2SEQ), the training set consists of 886,857 C, T pairs with C a complex sentence and T , the corresponding text.

for the pipeline models which first partition the input and then generate from RDF data (SPLIT-MULTISEQ2SEQand SPLIT-SEQ2SEQ), the training corpus for learning to partition consists of 13,051 M C , M 1 .


This architecture could also potentially be applied to conversational response generation to relieve the safe response problem, where the generative part can be an Seq2Seq-based model that generates response utterances for given queries, and the discriminative part can evaluate the quality of the generated utterances from diverse dimensions according to human-produced responses.

unlike the image generation problems, training such a GAN for text generation here is not straightforward.


Two facts based on the principle of GAN could be taken to explain this observation: On one hand, a discriminator with too low capacity (such as "Filter[1]") is less capable in distinguishing human responses from generated ones, which will backpropagate inappropriate signals that misleads the generator.

if the capacity of the discriminator is too high (such as "Filter[1, 2, 3]"), in the adversarial training, the training of the discriminator will converge too fast before the generator being sufficiently trained (Durugkar et al., 2016).


We note that this trick comes at a cost of worse result on the density estimation task, since part of the parameters of the full model are trained to optimize an objective that does not capture all the dependencies that exist in the textual data.

the gap between purely deterministic LM and our model is small and easily controllable by the α hyperparameter.


One interpretation of this argument applied to textual data is that factorizing the joint probability as p(x) = t p(x t |x <t ) provides the model with a sufficiently powerful decoder that does not need the latent variables.

our experimental results suggest that LSTM encoder may not be a sufficiently expressive encoder for VAEs for textual data, potentially making the argument inapplicable.


One of our main goals is to build a system that can create meaningful, diverse, and funny stories which apply to a broad audience.

in pilot tests with human players, we found that six of the original hint types restricted the variety of humor that can be generated by filling in their blanks, by: (i) not affording a variety of possibilities to fill in (hint types: color, silly word, exclamation) and subtlety in humor generation (number), and (ii) generally requiring the audience to have knowledge of cultural references and specifics (person name, place).


The success of the method is recently mathematically explained using the random walk on discourses model (Arora et al., 2016a).

there is a need to extend word embeddings to entire paragraphs and documents for tasks such as document and short-text classification.


This method tries to address the above problem.

it assumes that all words within a document belong to the same semantic topic.


Further, BoW V computes inverse cluster frequency of each clus-ter (icf) by averaging the idf values of its member terms to capture the importance of words in the corpus.

boW V does hard clustering using K-means algorithm, assigning each word to only one cluster or semantic topic but a word can belong to multiple topics.


If functional words such as some or any are ignored or represented as the same vector, then these sentences are to be represented by identical vectors.

the first sentence implies that there is a player who Tom did not meet, whereas the second sentence means that Tom did not meet anyone, so the sentences have different meanings.


Also, a categorical compositional distributional semantic model has been developed for recognizing textual entailment and for calculating STS (Grefenstette and Sadrzadeh, 2011;Kartsaklis et al., 2014;Kartsaklis and Sadrzadeh, 2016).

these previous studies are mostly concerned with the structures of basic phrases or sentences and do not address logical and functional words such as negations and connectives.


There are two reasons for this phenomenon: negations tend to be omitted in nonlogic-based features such as TF-IDF and the proportion of word overlap is high.

as logical formulas and proofs can handle negative clauses correctly, our method was able to predict the score correctly.


We will release the newly annotated data and the codes of the benchmark approaches at http://hlt.suda.edu.cn/~zhli.

due to the license issue, we may not directly release all the pseudo MWS datasets.


For better understanding of annotation heterogeneity, we summarize high-frequency differences among the three datasets observed and gathered during this study in Appendix A.

it is difficult to obtain a complete list of annotation correspondences among the three datasets, since there are too many lowfrequency and irregular cases.


Comparative Error Patterns When considering full analyses, we observe that our system still makes some errors in words where MADAMIRA is correct.

the number of times our system is correct and MADAMIRA is not is over twice as the reverse (MADAMIRA is correct and our system is not).


State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%.

due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.


Many derivational suffixes in English are of this type.

some patterns are subject to semantic, pragmatic, morphological or phonological restrictions.


This suggests that the network is indeed learning the correct set of possible nominalization patterns.

the information needed to correctly choose among these patterns for a given input is not necessarily available to the network.


We have focused on dependency parsing to demonstrate the utility of our approach as an economical and effective way to combat data sparsity.

we believe that the true benefit of this architecture will be more evident in speech processing as jamo letters are definitions of sound in the language.


Our replication confirms the result in general, and we additionally find that the advantage of LSTMs is even bigger for larger tagsets.

we also find that for the very large tagsets of morphologically rich languages, hand-crafted morphological lexicons are still necessary to reach state-of-the-art performance.


We also try to choose more effective examples for self-training and tri-training, by selecting training instances according to the base segmentation model score.

the segmentation performances do not get improved.


For readers understanding, we list the axioms in the order (1 to 7) they are used to solve the problem.


Datasets with span-based answers are challenging as the space of possible spans is usually large.

restricting answers to be text spans in the context passage may be unrealistic and more importantly, may not be intuitive even for humans, indicated by the suffered human performance of 80.3% on SQUAD (or 65% claimed by Trischler et al.


E.g., the AI2 Elementary School Science Questions dataset (Khashabi et al., 2016) contains 1080 questions for students in elementary schools; NTCIR QA Lab (Shibuki et al., 2014) evaluates systems by the task of solving real-world university entrance exam questions; The Entrance Exams task at CLEF QA Track (Peñas et al., 2014;Rodrigo et al., 2015) evaluates the system's reading comprehension ability.

data provided in these existing tasks are far from sufficient for the training of advanced data-driven machine reading models, partially due to the expensive data generation process by human experts.


This encourages us to build a semantic parser that produces an explicit representation of what the question is asking, if we want to make quantitative progress on the question set.

while it is not hard to formalize the semantics: it is not clear how to devise a compositional transformation from the original question to the formal semantics, since the meaning is dispersed throughout the discourse, such that neither the maximization nor the minimization can be locally derived from some subtree of the syntactic structure.


Because of this, it is feasible to use discriminative approaches Hosseini et al., 2014;Roy and Roth, 2015;Zhou et al., 2015;Koncel-Kedziorski et al., 2015;Mitra and Baral, 2016) that extract the quantities, featurize the question, and then perform a weighted search over the space of instantiated operator trees or equation templates.

it is not clear how one can extend these discriminative techniques to handle the complex semantics found in Examples 1 and 2.


Template-based approaches Zhou et al., 2015;Upadhyay et al., 2016), on the other hand, leverage the built-in composition structure of equation system templates to formulate all types of math concepts seen in training data, such as (1 − n 1 ) * n 2 = x in Figure 1.

they suffer from two major shortcomings.


In general the results illustrate that performances of the models increase with the length of the window.

we found that for SECT model, its mean performance reached the peak while standard deviations narrowed when window size reaches 10.